{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best w: [[ 0.01013554]\n",
      " [-0.01118452]\n",
      " [ 0.68269791]\n",
      " [-0.18625909]\n",
      " [ 0.56556456]\n",
      " [-0.76039839]\n",
      " [-0.0587618 ]\n",
      " [ 0.40240551]], best error: 0.7421407031499246\n",
      "Correlation coefficient: 0.6748460114148546\n"
     ]
    },
    {
     "data": {
      "image/png": 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O5eSTT6asrGxY+tqpq27lkZ+tY+7SUpZePyOu637nnXd44YUXWLJkCRdccEFc163Sk4hs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\nBwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10664ce80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run ridge_regression.py"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
